To evaluate the performance of WB models (100 hours training data), two metrics are utilized, namely PESQ (PESQ-WB, PESQ-NB (narrow-band, 0~4 KHz)) and STOI [1]}, [2]}. The comparison based on WB set is shown in Table REF . It can be seen that the dual-path encoder and DPRNN in \(\text{CEM}^+\)  bring an improvement in terms of feature utilization and frequency dependencies modeling with more arithmetic complexity. \(\text{GHCM}^+\)  improves the performance because the linear module has a wider receptive field with less computational complexity than the convolution. In \(\text{HM}^+\) , the significance-based VRD reduces the computation of CEM, and the introduction of cosine makes gates more accurate and instructive.
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